mobile phones for labor market intermediation: a multi-treatment experimental...
TRANSCRIPT
Mobile phones for labor market intermediation:
A multi-treatment experimental design
Ana C. Dammert Jose C. Galdo* Virgilio Galdo
Carleton University and IZA Carleton University and IZA The World Bank
[email protected] [email protected] [email protected]
Abstract
This paper investigates the causal impacts of integrating mobile phone
technologies into traditional public labor-market intermediation services on
employment outcomes. By providing faster, cheaper and up-to-date information on
job vacancies via SMS, mobile phone technologies might affect both the rate at
which offers arrive and the probability of receiving a job offer. Yet, in contrast to
agricultural or fishing markets where the speed of information matters, in labor
markets the scope of information might be more important due to the lack of
homogeneity of workers and firms. We therefore implement a social experiment
with multiple treatments that allows us to investigate both the role of information
channels (digital versus non-digital) and information sets (short [public] versus
enhanced [public/private]). The results show positive and significant short-term
effects on employment. Yet, it is not the technology by itself that leads to these
effects. Rather, it is the enhanced set of information about job opportunities,
transmitted through digital channels, which drives the positive impacts. As for
potential matching efficiency gains, the results suggest no (statistically) significant
effects associated with either information channels or information sets.
JEL classification codes: I3, J2.
Key words: labor market intermediation, mobile phones, ICT, field experiments, Peru.
* Corresponding author. We acknowledge financial support from the Inter-American Development Bank (IADB) and
the Social Sciences and Humanities Research Council of Canada (SSHRC). We thank SASE Asociacion Civil and the
Ministry of Labor and Social Promotion for their logistic and institutional support. Rene Castro and Minoru Higa
provided excellent research assistance. All errors are our own.
Contacts: Ana Dammert, Department of Economics and International Affairs, Carleton University, 1125 Colonel by
Drive, Ottawa, ON K1S5B6, Canada. Jose Galdo, Department of Economics and SPPA, Carleton University, 1125
Colonel by Drive, Ottawa, ON K1S5B6, Canada. Virgilio Galdo, The World Bank, 1818 H Street, NW, Washington,
DC, 20433.
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1. Introduction
Information, though often costly or incomplete in reality, is central to the functioning of
markets (e.g., Stigler 1961). Labor market transactions are not an exception as workers and firms
must dedicate resources and time to meet in the marketplace. Given the costly nature of job
searches, it has been long emphasized that the role of labor market intermediaries (LMIs), such as
public employment offices and temporary help agencies, can reduce search costs and adverse
selection problems (Autor 2001). Yet, despite their potential contribution to the functioning of the
labor market, these institutions remain relatively understudied (Autor 2009).
The unprecedented expansion of Information and Communication Technologies (ICT) in
the last two decades has enabled and shaped the existence of a new array of labor market
intermediaries, such as online job boards, social media sites, and e-recruiting firms, which have
invigorated research in this area. Evidence, largely based on U.S. data, has shown how the diffusion
of the Internet has allowed new labor market intermediaries to offer information access to a larger
pool of individuals at much lower costs (Stevenson 2009, Nakamura et al. 2009, Bagues and Sylos
2009). It still remains unclear, however, whether access to the Internet has positively affected the
duration of unemployment spells. While Kuhn and Skuterud (2011) showed that Internet job
searches were effective in reducing unemployment durations, Kroft and Pope (2009) and Kuhn and
Skuterud (2004) reports no effects. Evidence for developing countries is nonexistent.
While this promising research has addressed the impacts of Internet access on search
behavior and unemployment spells, nothing is yet known about the role of mobile phone
technologies on the efficiency of the labor markets. In fact, mobile phone technologies have not
been yet incorporated formally in the evaluation of labor market intermediation. The aim of this
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study is to provide evidence, analysis and insights on the causal impacts of (digital) public labor-
market intermediation in a developing-country setting.
Unlike the Internet, mobile phones have become the most rapidly-adopted technology in
developing countries due to the fact that the costs associated with the installation of mobile phone
towers is relatively low (Jensen 2010). Recent statistics for mobile phone penetration indicate that 8
of 10 people in the world had a subscription in 2011, up from 1 in 10 in 2000 (World Bank 2012).
Not surprisingly, credible non-experimental studies of fishermen in India (Jensen 2007) and
farmers in Niger (Aker 2010), Uganda (Muto and Yamano 2009), and India (Goyal 2010) have
shown how access to mobile phone service is associated with increases in arbitrage, declines in
price dispersion and increases in the number of markets over which farmers trade.
While there is a great deal of narrative evidence in regard to the promise of mobile phone
technology fostering economic development, well-identified empirical studies are scarce (Chong
2011, World Bank 2012). A distinctive feature of this study is its experimental design. We use as a
case study the public LMI system in Peru, a country that adopted an innovative e-government
initiative in labor intermediation.1 We implement a social experiment with multiple treatments as
part of regular (non-experimental) public labor intermediation services. This approach minimizes
considerable empirical difficulties due to ‘unobserved’ factors associated with participation and the
endogenous placement of mobile phones.
This study addresses four specific and related topics of interest. First, we exploit unique data
to analyze the determinants of digital job search in Peru by considering both Internet and mobile-
phone channels. Second, we evaluate the overall (digital and non-digital) effectiveness of public
LMI on reducing unemployment spells and improving the match quality of workers to jobs.
1 Peru’s mobile infrastructure reached a substantial coverage of 87 mobile phone subscriptions per 100 inhabitants, with
80 percent of them classified as prepaid subscriptions (ECLAC 2009).
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Evidence in this area is mixed, highly dependent on the research design and corresponds mainly to
developed countries where concerns about the efficiency of unemployment insurance systems have
led to a stream of studies in this field (e.g., Bishop 1993, Ashenfelter et al. 2005, Meyer 1995).
Third, we compare the effectiveness of digital versus non-digital labor market
intermediation within the public system. By providing faster, better and cheaper access to
information, mobile phone technologies might help poorly functioning markets to work better as
jobseekers can access relevant, up-to-date information on job vacancies via SMS. Yet, in contrast to
fishing or agricultural markets in which the speed of access to information matters, the technology
by itself might not be enough to efficiently connect workers to jobs.
Four, we analyze whether it is the technology or the set of information available to
jobseekers that matters most. In contrast to agricultural markets where there is a certain degree of
homogeneity to the commodities being exchanged, the potential gains of improving the scope of
information, rather than its speed, might be more dramatic due to the lack of homogeneity of
workers and firms. We then compare the impacts of short (public) versus enhanced (public/private)
information sets available to jobseekers while holding fixed the information technology.
From June 22, 2009 to September 1, 2009, the experimental sample was selected at the
initial registration filing for the normal inflow of applicants into Lima. Jobseekers that signed up to
receive public labor-market intermediation were randomly assigned to four treatment groups
according to two information channels (i.e., digital and non-digital intermediation) and the scope of
information they received (i.e., short [public] and enhanced [public/private] information sets).
From a theoretical standpoint, the improvement of access to information via mobile phone
service might affect the efficiency of labor market intermediation through several channels.
Economic theory predicts that the larger the cost of search, the less extensive will be the searches
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undertaken by workers at a given wage distribution (e.g., Stigler 1962). A direct effect of
introducing mobile phones in labor market intermediation is a direct fall in the costs of search as
workers can access relevant, up-to-date information on job vacancies via SMS. A second channel
through which improved information via mobile phones affects the labor markets is via better
arbitrage of prices (wages), which might lead to higher wages being offered to workers (Grossman
and Stigler 1976). Information in the labor market is, after all, capital that is produced at the lower
cost of search and, thus, yields higher returns than what would have been received in its absence. A
third channel through which improved information via mobile phones affects the labor markets is
by speeding the clearing of the labor market. Mobile phones allow information to travel instantly
and at lower costs. Standard search models predict that both the rate at which offers arrive and the
probability of receiving a job offer will increase as a result of search costs decreasing (Mortensen
1986). A fourth channel is by better matching workers to jobs due to enhancements in search and
coordination tasks (Aker and Mbiti 2010).
At the same time, some countervailing factors might affect the relevance of mobile phone
service in labor intermediation. The rise in the number of potential matches because of reduced
search costs would increase the minimum wage a worker will accept or the minimum productivity
an employer will accept (Pissarides 2000, Boone and Van Ours 2004). Moreover, the fall in search
costs may also lead to a fall in match quality due to an excess of applications of unemployed people
who belong to groups with chronic problems of employability (Lang 2000), or the emergence of
crowding-out effects from traditional search methods. If the introduction of mobile phone
technology exacerbates adverse selection in labor intermediation, matching in the labor markets
would rely more on personal connections than on formal intermediation mechanisms (Casella and
Hanaki 2010). Furthermore, there are some mitigating concerns about the effectiveness of mobile
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phone technology in developing countries due to the lack of human capital and due to the language
barriers between the users of the technology and the technology itself (Chong 2011). In all, the net
effect of the introduction of mobile phone technologies on unemployment duration and match
quality is an empirical question that will depend on the relative strengths of these channels.
Four main findings emerge. First, Internet and mobile phone job search channels seem to be
complements, fueled by the substantial growth of small Internet Café shops (‘cabinas de Internet’)
across most neighborhoods. Jobseekers with past labor-market experience and high expectations on
future job gains are more prone to use both digital channels when searching for jobs. In contrast to
Internet-based search, the use of mobile phones for job purposes is more related to disadvantaged
men and migrants. Second, setting aside the distinction between digital and non-digital channels,
we found a short-term statistically significant employment impact of 6 percentage points for the
government-sponsored LMI. Third, by comparing digital versus non-digital channels and short
versus enhanced information sets in a multiple treatment framework we find that it is not the
technology by itself that causes these positive impacts. Indeed, it is the enhanced set of information
about job opportunities, transmitted through digital means, which drives the positive employment
results. In contrast to fish and agricultural markets, the scope and extent of information sets seems
to be more relevant in the labor market. Fourth, independently of the information set and
information channel, we do not find (statistically) impacts of public intermediation on job matching
efficiency. This result can be explained by the ‘information-only’ intermediary nature of mobile
phones.
This study speaks directly to the recent literature on digital labor market intermediation that
is largely based on observational data for developed countries. Several studies in this area showed
how the diffusion of the Internet has allowed information on labor market opportunities to travel
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instantly and at a lower costs (Stevenson 2009, Nakamura et al. 2009), leading to a reallocation of
search effort among various job search activities (Stevenson 2009, Kuhn and Mansour 2011), and
as a result, to more diversified job search behavior (Kroft and Pope 2010, Cahuc and Fontaine
2009). By providing the first experimental evidence on the role of mobile phone technologies in
connecting workers to jobs, we expand this literature in important ways.
We contribute to the understanding of how the new ‘information economy’ (Freeman 2002)
is shaping new labor market institutions in developing countries. E-government initiatives are
transforming the way governments interact with citizens and organize their operations; yet, there
are no formal evaluations of their effectiveness. By documenting the impacts of mobile phone
technologies on job search outcomes, our findings contribute to an emerging body of empirical
literature that reports how the development and use of mobile phone services has brought new
possibilities for economic development. In this regard, a number of recent studies have addressed
the impacts of mobile phones in agriculture (e.g., Aker and Fefchamps 2013, Goyal 2010), health
(e.g., Dammert et al. 2013a, Pop-Eleches 2011), institutions (e.g., Yañez-Pagans and Machicado
2010), and financial markets (e.g., Karlan et al. 2010).
This study is organized as follows. Section 2 presents a background discussion about the
public intermediation system in Peru; section 3 discusses the research design, treatment and
treatment groups; section 4 analyzes the data and the baseline equivalence; section 5 analyzes the
determinants of digital job search; section 6 reports the empirical results; and section 7 concludes.
2. Background: Public employment services in Peru
Prior to 1996, the Peruvian Public Employment Services operated as a centralized
intermediation office, hosted at the headquarters and regional branches of the Ministry of Labor and
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Social Promotion, with the aim of connecting workers to jobs. The employer-employee matching
process was highly bureaucratic and done manually case-by-case without any regard for the
automatization of the data collection and dissemination of the information. Not surprisingly, the job
intermediation process was inefficient and costly. The average time it took for a worker to complete
the registration process was 4 days after completing a battery of aptitude, knowledge, and
psychometric tests (Ministry of Labor, 1998). As a result, only 3 percent of active jobseekers used
the public employment services in 1996, and in particular, they were those with chronic problems
of employability (Chacaltana and Sulmont 2003).2
In 1996, the Peruvian government launched an ambitious e-government initiative aimed to
modernize the role of the public employment agencies. With technical and financial support from
the Swiss government and the European Union, a new National Information System (SIL) was put
into place to support the adoption and design of e-government procedures, lower the administrative
burden, and to improve the efficiency of the intermediation process. Bureaucratic processes were
replaced by simpler and time-saving protocols, the compilation and dissemination of information
changed from manual to automated matching algorithms, centralized labor-market institutions were
replaced with decentralized offices through partnerships with not-for-profit institutions such as
NGOs and vocational schools, and more caseworkers were hired and trained in labor-market
intermediation (MTPS 1998, 1999, ICA 2005).
This active labor-market initiative started its operations in 1998 with the introduction of
CIL-PROEMPLEO, an LMI institution that offers three complementary services: intermediation,
information, and low-cost reemployment services. On the intermediation front, bureaucratic
practices was greatly simplified as participation only requires individuals and firms to register in-
2 The duration of unemployment for jobseekers using the public employment services was around 11 weeks, while for
those using other job search strategies was 7 weeks.
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person at any labor mediation office by filling a simple application form and answering a short
standard labor-market survey.3 This information is subsequently uploaded to SILNET, a
customized informatics system of job intermediation available only through an intranet. SILNET
has simplified enormously the search costs in time and effort for both firms and workers. The
registration process, for instance, went from 4 days prior to 1998 to less than an hour (MTPS 1999).
The computer software analyzes and matches workers’ skills and experience with job openings.
Once a suitable match is found, the jobseeker is contacted in person, by telephone or e-mail, if
available, in order to pick-up a letter of presentation from the Ministry of Labor with details about
the job vacancy. It is worth noticing that job matches are based only on job opportunities generated
from firms that have previously signed up on CIL-PROEMPLEO.
The information component aims to provide relevant information to both registered workers
and firms in topics such as wages by types of occupation, basic regulatory framework, demand in
specific occupations, training opportunities, occupational profiles, etc. Registered jobseekers are
also entitled to receive low-cost reemployment services such as workshops on writing CVs,
interviewing skills, and job-search strategies. Unlike other experiences, particularly in developed
countries, participation in these services is voluntary given the absence of an unemployment
insurance system in Peru.
Two years after the implementation of CIL-PROEMPLEO, the number of intermediated
workers significantly increased. While in 1996, the total number of employee-employer
intermediation was 10,275 at the national level, it jumped to 22,764 in 1998. The corresponding
3 The information collected from jobseekers include standard demographic variables, schooling, training participation,
computing knowledge, foreign-language knowledge, driver’s license, labor market experience, and current labor-
market situation. Firms provide contact information (address, contact name, telephone, location, etc.), detailed
description of the job vacancy (work schedules, salary, location, main responsibilities and tasks), and job requirements
(education, experience, computing and language knowledge, years of experience, driver’s license).
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numbers for metropolitan Lima, which represents more than 55 percent of the total intermediation,
were 7,914 and 12,682, respectively (Ministry of Labor 1999). All in all, the number of
unemployed persons using the public intermediation system has increased significantly in the past
decade from three percent in 1997 to 14 percent in 2007. Likewise, the number of job vacancies
that has been offered through the public system has increased by five times from 12,707 in 1997 to
66,691 in 2007. According to administrative data, the average cost for each intermediated worker
reaches US$ 25 dollars, which reflects a US$ 191 dollars per capita of social savings with respect to
the cost of using private employment agencies (MTPS 1998).4
Administrative data also shows that the effective rate of intermediation was quite steady
over time. As a percentage of the labor supply, CIL-PROEMPLEO was able to intermediate one-
out-of-four workers, with small variations over time. As a percentage of the number of job
vacancies, there is more variability ranging from 60 to 80 percent between 1998 and 2007. One
potential explanation for this flattened profile is the relatively small number of job vacancies that
are still advertised via the public intermediation system. Indeed, only 15 percent of firms located in
metropolitan Lima were registered with CIL-PROEMPLEO in 2004 (Vera 2006).
A second wave of reforms in the operation of the Peruvian public intermediation system
occurred in 2004 when the online version of CIL-PROEMPLEO started its operation in an effort to
expand the adoption of information and communication technologies in the labor market.5 In
practice, this e-government innovation allows all registered jobseekers and firms online access to
CIL-PROEMPLEO services, provided they have access to the Internet.6 The main attribute of this
4 A common practice for private employment agencies in Peru is to charge each worker half of the monthly salary they
would receive in the new job. 5 The service is free and accessible to anyone with a computer at http://www.empleosperu.gob.pe.
6 It is estimated that 34 percent of registered jobseekers make use of this electronic version of the program (MTPS
2006).
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online service is that workers and firms can upload their information and exchange suitable matches
without the intervention of any caseworker. In 2005, one year after the full operation of this
electronic version, 59,137 people used the online version, along with more than 5,000 firms that
offered 32,000 job vacancies (MTPS 2006). There is no available data, however, on the rate of
labor intermediation for this component as firms and workers exchange freely without the direct
intervention of CIL-PROEMPLEO.
3. Research design, treatment, and treatment groups
A primary concern in the literature of labor-market intermediation is the role of ‘selection
on unobservables’ that has plagued non-experimental empirical studies (e.g., Meyer 1995, Kuhn
and Skuterud 2004). In this setting, adverse selection is a key issue since public employment
services seems to be populated with unemployed individuals with chronic problems of
employability (e.g., Autor 2001 and Thomas 1997).7 Moreover, the introduction of mobile phone
technology in labor market intermediation might tend to exacerbate selection issues due to
endogeneity problems associated with technology adoption and participation (Heeks 2010).
A distinctive feature of this study is its experimental design with multiple treatments that
minimizes considerable empirical difficulties associated with both self-selection in labor
intermediation and the endogenous placement of mobile phones. Early in 2009, we signed a formal
agreement of cooperation with the Ministry of Labor and Social Promotion. This agreement
allowed us access, in real time, to the SILNET intranet system. The field experiment was
implemented as part of the regular (non-experimental) public intermediation services, which affords
us three main advantages. The experimental sample is formed exclusively with new registered users
7 Thomas (1997), for example, shows that the positive relationship between public employment services use and
unemployment spell duration is biased because of the lack of control for the timing of unemployment.
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of the public employment system and that greatly minimizes 'selection on unobservables' issues.
Second, new registered users are not aware of their participation in an experiment and the
exogenous manipulation of the economic (intermediation) environment. This minimizes potential
bias induced by the presence of 'John Henry' effects or, more generally, 'Hawthorne' effects (List
and Rasul 2010). Finally, we automatically gained access to administrative baseline data. On the
other hand, due to institutional restrictions we were only allowed to leave the control group
individuals out of intermediation for three months after registration in the LMI system, and thus, we
were only able to measure short-run treatment effects.
The random assignment was carried out on a daily basis (excluding weekends and
holidays) from June 22, 2009, to September 1, 2009. The experimental sample was selected at the
initial registration filing for the normal inflow of applicants into Lima after excluding registered
individuals who do not own mobile phones or hold occupations with very high turnover rates; i.e.,
unskilled persons. The treatment consisted of three months of subsidized job search assistance in
which individuals' labor profiles were matched with available job vacancies. The matching
algorithm was able to map specific job requirements (e.g., gender, age, schooling level, prior
experience, and skills) to individuals’ profiles. Jobseekers were contacted immediately when a
positive match was found.
We randomly assigned new registered users of CIL-PROEMPLEO into three different
groups: non-digital treatment group, digital treatment group, and control group, following a random
allocation of 30, 40, and 30 percent, respectively. The non-digital treatment group was subject to
the standard CIL-PROEMPLEO intermediation practices. To avoid any disruption (or caseworker
bias) the names of selected members in this group were kept away from case workers. In contrast to
this treatment group, the digital treatment group was exposed to a technological innovation aimed
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to reduce job search costs. Jobseekers assigned to this group were informed about job-market
opportunities that match their labor profile through digital services; i.e., delivery of SMS messages
to their mobile phones. The difference between non-digital and digital treatment groups is that the
former is contacted in-person or by phone following the standard protocols from CIL-
PROEMPLEO, while the latter is electronically contacted via text messages. Independently of
whether individuals belong to the short- or enhanced-digital treatment groups, the framing of the
information sent to jobseekers via SMS follows a standard structure and is limited to the description
of the occupation and contact information.8 It is worth mentioning that the experimental design
provided evidence only on subsidized job search assistance and does not incorporate elements of
job search requirements, i.e., enforcement and verification, because of the absence of an
unemployment insurance system in Peru. In other studies that combine both aspects, it is more
challenging to identify what aspects of the experiments induced the change in outcomes (Meyer
1995, Black et al. 2004, Ashenfelter 2005). Finally, control group individuals were removed
temporarily from the information system for a period of three months.
In contrast to recent studies that has credited the technology (mobile phones) with
significant increases in arbitrage, declines in price dispersion and increases in the number of
markets over which farmers trade in developing countries (e.g., Aker 2010, Jensen 2007), it is not
clear that jobseekers will shorten their unemployment spells by having access to faster and cheaper
labor-market information. In Appendix A, we present for illustration purposes a theoretical model
with endogenous search effort in both public and private search channels that shows uncertain
effects of technology-driven search efficiency improvement on unemployment spells. The
uncertainty emerges because patient jobseekers could wait for a better wage offer or because of
8 A literal reproduction of an SMS message says: “PROEMPLEO. Hostess wanted Restaurant Amador. Av. La Mar
#3453 Lince Tel 3038145. Contact: Elizabeth Bartra”.
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crowding-out effects in the alternative private search channel. Therefore, we turned to the
experimental data to compare the employment effects of digital versus non-digital experimental
treatment groups relative to a control group.
Furthermore, a key feature of CIL-PROEMPLEO is that registered jobseekers can be
intermediated only with job vacancies from firms that signed up on the public intermediation
system. This attribute could be inefficient as the majority of job vacancies in Lima are advertised
through alternative channels such as newspaper ads, job boards or private employment agencies
(Vera 2006).9 Therefore, to test whether it is the technology by itself or the set of information
available to jobseekers that matters most, we implemented a second set of randomization that
manipulates the size of the information sets available to jobseekers while holding fixed the
(information) technology.
Within the digital treatment group, individuals were randomly assigned into two different
groups: short-digital treatment group and enhanced-digital treatment group. The former is matched
only with job-opportunities generated within the CIL-PROEMPLEO system, i.e., the public
information set, while the latter is matched with both CIL-PROEMPLEO vacancies as well as job
opportunities posted on alternative channels such as national newspapers ads and not-for-profit
private employment agencies, i.e., public/private information sets. The allocation ratio was 1:1.5 for
both groups. Thus, the comparison of these short- and enhanced-digital treatment groups allows one
to test the impact of expanding the set of information available to jobseekers while holding fixed
the information technology.
In total, 1,280 individuals were randomly assigned from June 22 to September 1, 2009 to
four different groups, of which 354 corresponded to the control group (Dc), 344 to the standard non-
9 According to a 2003 survey of formal firms located in metropolitan Lima, advertisement of job vacancies is mainly
done via newspaper ads (54 percent).
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digital treatment group (DT1
), 188 to the short-digital group (DT2
) and 303 to the enhanced-digital
treatment group (DT3
). By comparing the treatment impacts across these different experimental
groups we are able to evaluate the overall impact of CIL-PROEMPLEO on the employment status
of registered participants, evaluate the effectiveness of adopting digital technology (i.e., SMS) in
labor-market intermediation, and test the impact of expanding the set of information available to
jobseekers while holding fixed the information technology. We acknowledge that our sample
represents a particular subset of the unemployed population, so external validity of the findings
should be taken with caution.
4. Data and baseline equivalence
The baseline dataset contains information for 1,189 individuals, which implies an attrition rate
of 7 percent relative to the original sampling design.10
This feature adds to the quality of the dataset
used in this study since attrition bias is common in these types of settings. A critical step in the
estimation of the causal treatment effects is to analyze the effectiveness of randomization in
balancing the distribution of covariates across all treatment groups. Table 1 depicts baseline socio-
demographic and labor-market information for all experimental groups, collected from
administrative sources at the initial registration filing, and complemented with information from a
customized labor survey implemented right after the registration process. Columns 1 to 4 show
descriptive statistics for each one of the four experimental groups, while columns 5 and 6 show the
p-values for the t-test (treatment versus control) and the F-test of equality of means across all four
experimental groups.
10
The rate of attrition was similar in all treatment groups and it is not statistically related to any particular socio-
demographic variable.
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Panel A shows that the average individual in our sample has completed a high-school
education, is younger than 30 years old, and single. There is a slight disproportion in the rate of
enrollment by gender, as 55 percent of registered users are men. Only 30 percent of users have
offspring, which indicates the average user of the public intermediation is relatively young.
Moreover, an analysis of household indicators reveal that the average individual in this data have
access to basic infrastructure and own household assets. The p-values for all variables, except for
age, do not reject the null hypothesis of the equality of means across all experimental groups.
Panel B reports basic statistics for a rich set of baseline labor-market characteristics. The
unemployment spells for all groups is statistically balanced (94 days), which constitutes an
important piece of information since the reliability of several non-experimental studies in this area
has been questioned because they exploited only variation of intermediation usage without
controlling for the timing and duration of the unemployment spell (see for instance Thomas 1997).
Moreover, 80 percent of the sample has previous job experience, mainly in the private sector, while
12 percent are considered as a discouraged worker. The average monthly income and weekly hours
of work are 520 soles and 35 hours, respectively. Around one fourth of the sample had fringe
benefits including health insurance, accident insurance, and pension plans, while 50 and 16 percent
worked as white- and blue-collar workers, respectively. The p-value of the F-test for the equality of
means is above 0.05 for all variables except for the share of people who had worked under formal
contracts.
Panel C reports novel information on ICT usage and job search strategies among jobseekers
subject to public labor-market intermediation. The average number of search methods used by the
sample is close to two. We find that 96 percent of the sample regularly use mobile phones, while 49
percent do so for job search purposes, and in particular, to contact prospective employers (57
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percent), private (26 percent) and public (11 percent) labor-market intermediaries. Further, 84
percent of the sample has experience using the Internet, which is a relatively high percentage with
respect to the population. Sixty-one percent of the sample used the Internet for job search purposes.
Importantly, 4 out of 5 Internet-based searchers access the Internet from Internet café shops where
for a small amount of money they can rent computers with online access. Only 25 percent of the
sample access Internet from home. Moreover, the data show that on average our sample spent 0.9
hours-per-week on public search channels, which accounts for only 20 percent of the total weekly
search time. This means that job search in Peru is mainly done outside of the public intermediation
system since the weekly (average) number of hours devoted to active job search is 4.5. The p-value
of the F-test for the equality of means does not reject the equality of means for all ICT-related
variables.
In sum, the statistical analyses performed on a large set of personal and household
covariates suggest that the sample of individuals assigned to all different groups were drawn from
the same population. This gives credibility to the experimental design.
5. Determinants of digital job search in Peru
In this section, we use the baseline information to assess the determinants of digital labor-
market intermediation for our sample of jobseekers irrespective of their treatment assignment. This
analysis sheds light on the role of socio-demographic and labor-market characteristics on
influencing digital job search, the complementarity between Internet and mobile-phone job search
channels, and how previous exposure to digital technology affects digital job search.
Table 2 shows the marginal effects from a probit model that estimates the likelihood of
digital job search conditional on a rich set of covariates. Each column corresponds to a different
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outcome of interest. In column 1, the dependent variable takes the value 1 for those individuals who
have used either Internet or mobile phones for job search purposes, 0 otherwise, while columns 2
and 3 considers Internet and mobile phone job search channels separately. Standard errors are
presented in parentheses.
By looking at Panel A in column 1, one is tempted to conclude that individuals’ socio-
demographic characteristics seems to play no role on the likelihood of (overall) digital job search in
metropolitan Lima since no variable is statistically significant at the 5 percent level. Yet, a closer
look at columns 2 and 3 reveals that indeed some personal characteristics matter depending on the
digital technology used. Two variables emerge as statistically significant predictors for Internet job
search: presence of siblings and poverty status, the former possibly associated with spillover effects
from children’s exposure to Internet at schools, and the latter associated with barriers to technology
access due to income constrains. In fact, column 2 shows that the likelihood of using the Internet
for job search purposes is almost 15 percentage points lower for poor households than that for non-
poor ones, and 19 percentage points higher for individuals with offspring relative to those without
them. The positive relationship between Internet job search and income is in line with international
evidence for developed countries (e.g., Stevenson 2009). Column 3, on the other hand, shows that
gender, schooling attainment, poverty and migration status of jobseekers matters for mobile phone
job search channel. Men are 10 percentage points more likely to search for jobs using mobile
phones than that for women, and an extra year of schooling is associated to 1.6 higher percentage
points for mobile-phone job search. Likewise, the poor (and migrants) are 14 (and 9) percentage
points more likely than non-poor (non-migrants) to use mobile phones during the job search
process. Overall, these results suggest that poverty status is the main factor that differentiates the
use of mobile phones from the Internet when selecting a job search channel. From a policy
- 18 -
perspective, this result highlights the importance of mobile phones for vulnerable populations that
cannot access to Internet in developing countries due to income barriers.
By turning our attention to Panel B, one observes that having previous job experience and
being optimistic about future job-market prospects positively affects the likelihood of digital job
search for both Internet and mobile phones channels. Those who had never worked show a 30-
percentage point lower probability of digital job search with respect to those with previous job
experience, while jobseekers that are optimistic about the probability of finding a job in the near
future are 16 percentage points more likely to search for jobs using digital technologies. The former
result suggests that past labor-market experiences have made workers more aware of the
possibilities of digital technologies, while the latter reinforces the findings in Dammert et al.
(2013b) that shows a causal link between digital labor intermediation and job gain expectations.
This last evidence suggests a virtuous circle between digital labor-market intermediation and job
gain expectations in developing settings, which matters because expectations are a meaningful
predictor of subsequent work status (Stephens 2004) and are associated with job search effort
(Diagne 2010) and wage growth (Campbell et al. 2007). Finally, no other labor-market variable
such as the type of worker or job-quality attributes matter.
In Panels C and D, one observes how past exposure to Internet and mobile phones affect
the likelihood of using them for job search purposes. The results show that digital experience does
not matter for mobile-phones searchers. In fact, having computer and Internet literacy or owning a
mobile phone for few months, three or more than five years, does not make any difference in the
usage of mobile phones for job search purposes. On the contrary, Internet-based job search is
greatly affected by the availability of a computer at home (likelihood increases by 52 percentage
points), length of computer experience (22 percentage point increase for those who have used a
- 19 -
computer for more than five years), and the existence of Internet cafe shops in the neighborhood
(69 percentage points). Evidence on the first two variables is not surprising and it is in line with
findings from other studies that are based on data from developed countries (e.g., Stevenson 2009,
Autor 2001). The last variable, however, provides new insights on the growing importance of
Internet cafes in developing settings. This type of business has blossomed in recent years, although
its impacts on the marketplace are practically unknown. From a policy perspective, this result
suggests that providing incentives for the expansion of this type of small business might improve
the efficiency of the labor markets in countries where a home connection to Internet is still far from
the norm in reality.
Finally, Panel E shows a positive correlation between Internet and mobile-phone search
channels, which suggest a complementarity relationship between them. The likelihood of using cell
phones for job search purposes is 40 percentage points higher for individuals who use the Internet
for job search relative to those who do not, while it reaches 35 percent in the other direction.
In sum, this section has uncovered evidence on key covariates that affect the access and
usage of digital job search channels in Peru. Poverty and the lack of availability of Internet cafes
seems to be the main barriers for Internet-based job search, while the presence of offspring, a
computer-at-home, labor-market experience and high expectations on job gain prospects are
positively related to this activity. The use of mobile phones for job search purposes, on the other
hand, seems to be more idiosyncratic with respect to Internet-based search, although it is clear that
disadvantaged men and migrant individuals are more prone to use it. Only two variables have a
consistent positive effect for both digital channels: labor-market experience and job gain
expectations, variables that are usually overlooked in the analysis of digital job-search channels.
- 20 -
6. Empirical framework and results
To estimate the overall effectiveness of public labor intermediation in Peru we implement a
standard parametric OLS regression for individual i in experimental (day) group j,
0 1 2'ij i ij j ijY D X (1)
where ijY is the particular outcome of interest (employment), iD denotes a treatment indicator that
receives 1 for all individuals subject to labor intermediation independently of the information
channel and information set they were assigned , 0 for those in the control group. ijX denotes a set
of socio-demographic and labor-market baseline covariates, while ij is the error term. Given we
have as many experimental sets as different days the experimental sampling lasted, we incorporate
experimental group (date) fixed effects, j , to control for intra-day variation in the treatment
allocation. The coefficient of interest is 1 and represents an intent-to-treat parameter. The main
outcome of interest is employment in the month of reference. In the context of our design,
regression analysis is motivated by the availability of a rich set of baseline covariates that could
reduce the size of the standard errors for the point estimates, the need to incorporate clustered
standard errors due to intra-day variation in the random allocation, and a multiple treatment design
that can be incorporated smoothly within this framework.11
11
An alternative option would be to implement a standard parametric difference-in-differences model. Yet, the
employment outcome in the baseline period refers to the week of reference, while the follow-up data captures monthly
employment for each one the first months following treatment. One should not expect, however, to have different
results given the strong balance in outcomes and covariates in the baseline period. In fact, we implemented an
unrestricted difference-in-differences model (Lalonde 1986), which shows similar results. Table Appendix A1 reports
these estimates. On the other hand, and because continuous data on unemployment spells are not available in the
follow-up period, we cannot estimate complementary duration (hazard) models.
- 21 -
To evaluate the effectiveness of adopting digital technology in labor-market intermediation along
with varying sets of information available to jobseekers, we expand equation (1) and implement the
following complementary model,
1 2 3
0 1 2 3 4'T T T
ij i i i ij j ijY D D D X (2)
where 1 2 3, and T T T
i i iD D D denote treatment indicators for the non-digital, short-digital, and
expanded-digital treatment groups, respectively. The base category is the control group. The
coefficients1 , 2 , and 3 represent intent-to-treat parameters of interest. By comparing the
magnitude and significance of 1 and 2 we are able to test the impacts of digital intermediation
holding fixed the set of information, while by comparing 2 and 3 we test the impacts of
expanding the set of information while holding fixed the information technology. Given the 3-
month treatment duration, the follow-up survey asks individuals to respond on their employment
status each month after assignment to treatment. Thus, we report three different estimates
corresponding to the first, second and third month following the treatment assignment.
6.1. Main results
Table 3 reports the OLS short-run impacts of public labor-market intermediation on
employment status one, two and three months following the intervention. The upper panel shows
the overall impacts (equation 1) without considering digital versus non-digital or short- versus
enhanced-information sets treatment assignment. Clustered standard errors by date are shown in
parentheses. Experimental results show that public labor-market intermediation has positive
impacts on the employment status of job searchers. These effects are somewhat above 6 percentage
points and statistically significant at the 5 percent level for the first month following labor-market
- 22 -
intermediation. This impact equals a 17 percent increase with respect to the baseline mean outcome.
A comparison of columns 1 to 3 show that these positive effects are robust to the inclusion of a rich
set of baseline covariates (column 2) and experimental groups fixed effects (column 3). This is
expected given that randomization at the individual level have led the balance of baseline covariates
between the treatment and control group individuals as shown in Table 1. The magnitude and
statistical significance of the employment impacts lessens two and three months after treatment.
Columns 4 to 6 show that the magnitude of the treatment effects reaches 5.5 percentage points and
is statistically significant at the 10 percent level two months following the intervention. For the
third month, no statistically significant impacts are observed (columns 7 to 9). Overall, Panel A in
Table 3 shows positive and statistically significant short-run employment impacts for public labor-
market intermediation in Peru, albeit these impacts are of lower magnitude with respect to the non-
experimental evidence presented by Sulmont and Chacaltana (2003) in their analysis of earlier
cohorts participating in the CIL-PROEMPLEO program.12
Two features related to this intervention might explain this profile for the employment
impacts. First, due to institutional constraints, the control group individuals were left out of
treatment for three months, and thus everyone in the sample was potentially subject to labor-market
intermediation starting the fourth month. Thus, we are dealing with a short-term intervention in
which the impacts of the third month of treatment might not be observed because of the timing of
the intervention. Second, in general, users of the public labor-market intermediation are individuals
with above-average labor-market attachment problems (e.g., Autor 2001 and Thomas 1997), and
12
Chacaltana and Sulmont (2003) used a sample of 153 individuals that were successfully intermediated in April 2001
as the treatment group. The comparison group comes from a representative employment survey from which 275
nonparticipant individuals, who were unemployed in April 2001, were drawn. Some outcomes of interest were
measured and compared one year later using standard parametric matching techniques. The authors show significant
gains in wages and employment for the treatment group, relative to the control one, one year later.
- 23 -
thus, it is possible that those who were not able to get a job during the first two months after the
start of the intervention are those particularly prone to having chronic problems of employability.
Panel B in Table 3 reports the intent-to-treat parameters following equation (2). Relative to
the control group, the non-digital treatment group (DT1
) shows positive, but not significant
treatment effects in months one (3.4 percentage points) and two (4 percentage points). On the
contrary, the digital channel seems to explain the overall significant impacts we found for the
public intermediation system. By looking closely at the last two rows in Table 3, one realizes that
within the digital channel is the enhanced-digital treatment group (DT3
) which shows the strongest
impacts in month one (8 percentage points) and month two (7 percentage points), both statistically
significant at the 5 and 10 percent level, respectively. The magnitude of these impacts is equivalent
to a slightly above 20 percent increase with respect to the baseline measure of employment. The
short-digital treatment group (DT2
), on the other hand, has weaker impacts as it is statistically
significant only at the 10 percent level in month one, while in month two it loses statistical
relevance. Neither group shows significant impacts in month three. All of these findings are robust
to the inclusion of a rich set of socio-demographic and labor-market baseline characteristics.
Likewise, these results are robust to the inclusion of experimental date fixed effects and clustered
standard errors.
How can one explain these results? According to information theory, the value of
information is determined by three important factors—novelty, confidence, and ability and
willingness to act based on the new information—all of which involve different forces and trade-
offs (Hirshleifer and Riley 1992). In our view, the distinctiveness between short [public] and
enhanced [public/private] information sets involves a novelty factor since historically the standard
public intermediation system has operated only with information from a limited group of low-
- 24 -
quality firms. Thus, individuals in the enhanced-digital treatment group received on average, not
only more information, but also qualitatively different information coming from firms that do not
normally register with the public system, all of which makes more valuable the information
received. Put differently, it is the value of the information generated by the novelty of the
information, along the higher number of SMS received by jobseekers, which might explain the
positive impacts on employment.
It is important to highlight that expanding the information set does not automatically
translated into “better” outcomes since more information is not necessarily more valuable, all else
held constant. One needs to recall that the typical user of the public labor market intermediation has
above-average problems of employability, and therefore they could have developed strong initial
beliefs about the value of the information, a genuine distrust of information coming from public
sources, or both. If that is the case, they might not believe in the information or act automatically in
response to more information. Evidence from behavioral economics, for example, suggests that
individuals with poor past experiences have difficulty interpreting subsequent new information,
ignore new information altogether or misread it as initial information expectations tend to anchor
one’s processing of information (Griffin and Tversky, 1992).
In sum, these findings suggest that it is not the technology by itself that causes the positive
effect of labor-market intermediation on employment. It is an enhanced set of information about job
opportunities, transmitted through digital channels, which drives the positive employment results.
Unlike agricultural or fishing markets where the speed of the information matters for the adjustment
of price and quantities (e.g., Aker 2010, Jensen 2007), in the labor markets the scope and size of the
information sent to jobseekers via SMS seems to matter most.
- 25 -
6.2 Heterogeneous Impacts
In this section, we analyze heterogeneous treatment impacts across three policy-relevant
variables of interest: gender of participants, poverty status, and work experience. This analysis
focuses on overall impacts rather than on particular treatments, since the latter might be asking too
much from the data given the sample sizes. The gender dimension is important in its own, as a body
of empirical evidence has shown strong gender gaps against women in the Peruvian market place
(e.g., Ñopo 2012). The results reported in Table 4 shows negligible and not statistically significant
impacts by gender. From a policy standpoint, this suggestive result shows that the public
intermediation system is not reproducing inequalities against women in the marketplace.
Moreover, we address whether the public intermediation system has differential
employment impacts depending on participants’ previous job experience. This is policy relevant
since adult jobseekers who have never worked are the most prone to fill the ranks of the inactive
population. We define the discrete variable of interest as 1 for those who have never worked, 0 for
otherwise. The results shown in the middle panel of Table 4 reveal statistically significant impacts
from previous job experience. Jobseekers who have never worked shown a 16 percentage point
difference in month one with respect to those who have work experience. This result reveals that a
lack of labor-market experience constitutes a resilient barrier that is difficult to overcome by public
labor-market intermediaries. No significant impacts were found for months two or three.
Finally, we analyze whether public labor-market intermediation is benefiting most the poor.
For that purpose, we constructed a poverty index based on more than 15 variables related to house
infrastructure (e.g., access to toilet, sewage) and household assets (e.g., computer, car) after
combining the data using factor analytic methods. This continuous poverty index represents a proxy
for long-run economic status. We use this index to define three categories: poor (first quartile),
- 26 -
middle (second and third quartile), and high (fourth quartile). Results reported in the bottom of
Table 4 show a well-defined pattern: there is a positive relationship between treatment and poverty
status. In the first month, for example, where most of the employment impacts are observed, the
point estimates associated to the poor (5 percentage points) are two times lower than those for the
upper group (10 percentage points). This result suggests that those who are most in need do not
benefit more from the public intermediation system. These differential impacts, however, are not
statistically significant at the 5 percent level, and thus, we can only take this as suggestive evidence.
6.3 Job-matching Efficiency
The power of labor intermediation on matching efficiency has been discussed more than
once in the literature since it is expected that efficiency gains are associated with formal
intermediation mechanisms that breakdown traditional networks and geographic barriers (e.g.,
Freeman 2002). In this regard, Bagues and Sylos (2009), for instance, found that the monthly wages
of graduates from universities that had a job board for their graduates increased by 3 percent
compared to those who did not. Inspired by this line of inquiry, we considered two matching
efficiency indicators as outcomes of interest: monthly earnings and matching skills. The latter is
defined as a dummy variable that takes the value 1 when there is an alignment between job tasks
and workers’ qualifications, 0 otherwise. We use only the subsample of individuals who are
successful in getting a job when estimating the impacts for these two variables. Panel A (B) in
Table 5 shows the earnings (matching skills) treatment impacts. Within each panel, the first row
corresponds to overall impacts of public intermediation, while rows 2-5 report the estimates for
each one of the treatment groups, relative to the control group.
- 27 -
Some patterns emerge from this analysis. By looking at the first panel, we observe that the
impacts for monthly earnings increase overtime, which suggest that those who become employed
immediately and right after the intermediation begin to have lower reservation wages than those
who got jobs later on. This result is consistent with standard predictions of theoretical search job
models (e.g. Mortensen 1986). However, these point estimates are not statistically significant and
thus they should be taken only as suggestive evidence. A second pattern we observe in this panel is
that across all three months following treatment, the enhanced-digital treatment group shows the
largest earnings impacts (30 soles, or 22 percent of the baseline earnings), while the short-digital
treatment group shows the lowest. This suggestive evidence reinforces the previous discussion
about the importance of the extent and scope of the information set in labor-market intermediation,
as that seems to be more relevant than the transmission channel itself. The availability of a larger
dataset or lengthier treatment duration would have helped us to provide more definitive results.
From an international perspective, the empirical literature shows ambiguous effects for LMIs on
matching efficiency, with some studies showing positive impacts on earnings (e.g., Bagues and
Sylos 2009), and others a zero effect (e.g., Kroft and Pope 2009).
By looking at the lower panel, we observe no significant impacts of labor-market
intermediation on job-matching skills for the overall program and for each particular treatment
considered. One potential explanation is that, unlike the Internet, mobile phone intermediation does
not necessarily improve the ability to screen applications, as it works as an ‘information-only’
intermediary channel. From an institutional standpoint, this result is not surprising given the high
levels of underemployment by qualification that affects the Peruvian labor-market. According to
Yamada (2010), the incidence of underemployment affects more than 60 percent of the work force.
This apparent divorce between individuals’ qualifications and job tasks is particularly manifest for
- 28 -
those less successful in the marketplace. In short, three months of public intermediation is not
enough to overcome this structural problem in the Peruvian labor-market.
6.4 Impacts on additional intermediate outcomes
Motivated by the complementarity between Internet and mobile-phone search channels
uncovered in section 5, we investigate the causal impacts of digital labor-market intermediation on
Internet-based search effort. The new outcome variable is defined as 1 for those who have used the
Internet for search purposes within the three months following labor-market intermediation, 0
otherwise. Columns 1 and 2 in Table 6 show these results with clustered standard errors in
parentheses. We observe a positive statistical relationship between mobile-phone labor market
intermediation and Internet-based job search, which is explained by the enhanced-digital treatment
group. On average, this particular treatment lead to an increase of 7.9 percentage points (13
percentage increase) in Internet-based job search results, a result that is statistically significant at
the 10 percent level. Both the non-digital and short-digital treatment groups also show positive
point estimates, although they are not statistically significant different from zero. This evidence
reinforces the uncovered evidence that Internet and mobile-phone search channels are
complements.
To analyze whether digital labor-market intermediation lead to crowding-out effects of
traditional search channels, we analyzed the causal impact of digital intermediation on newspaper
ads, friends and family referrals, among other traditional search channels, using the same
parametric framework estimated over the sample of unemployed individuals who are actively
- 29 -
looking for jobs in the week of reference.13
The results reported in columns 3 and 4 in Table 6 show
no evidence in this regard. The intent-to-treat parameters are not statistically significantly different
from zero for all treatment groups. In contrast to evidence from developed countries that show how
the lengthy penetration of Internet has led to a reallocation of search effort among various job
search activities (Stevenson 2009, Kuhn and Mansour 2011), we do not find similar evidence for
our short-length digital treatment intervention.
7. Conclusion
This study exploited a multi-treatment experimental design implemented as part of the
regular (non-experimental) public intermediation system in Peru to investigate the extent to which
public labor-market intermediation influences employment and job matching efficiency. Particular
attention was given to investigate the interaction between the speed (digital/non-digital channels)
and the scope of information (small/enhanced sets), given countervailing forces emerging from
standard theoretical employment search models. This study showed that public labor-market
intermediation causes positive and statistically significant employment impacts equivalent to 6
percentage points, relative to control group individuals. This positive effect, however, dissipated
three months later, timing that coincides with the beginning of labor-market intermediation for the
control group.
An important finding reported in this study is that, in the labor markets, the scope and
novelty of the information travelling through digital means seems to be more important than the
speed of information itself. Indeed, the use of an enhanced-information set that combines
information from public and private sources involved a novelty factor since historically the
13
In contrast to the Internet-based outcome, traditional search methods in the follow–up survey were asked only for the
week of reference and only to the subsample of unemployed people.
- 30 -
standard public intermediation system has operated only with information from a limited group of
(low-quality) firms. As for potential matching efficiency gains, the results suggest no statistically
significant effects associated with information channels or sets. Overall, we reported (statistically)
weaker impacts for SMS technologies in the labor market relative to previous studies that mainly
focused on agricultural markets and outputs.
From a policy standpoint, this research has shown the feasibility and value of integrating
new information technologies in the old, traditional labor-market intermediation services. That is
perhaps the main policy contribution of this research as it highlights how traditional LMIs can take
advantage of the rapid expansion of mobile phone technologies and catch-up themselves by using
SMS applications as a delivery platform. A particular lesson emerging from this study is that the
Peruvian public intermediation system could improve its efficiency if the restriction to operate
based on a limited set of firms that have previously signed up to the system is relaxed. In this
regard, jobseekers could benefit more if the public LMI operator also incorporated information
generated outside the system, such as from online job boards and newspapers ads.
Booming mobile phone connectivity in developing countries offers a unique opportunity to
counteract the Internet access gap against vulnerable populations. In this regard, this research has
shown that both Internet and mobile phone search channels are complements, although
disadvantaged men and migrants are more prone to use the latter for job search purposes. Likewise,
particular attention should be given to those who lack labor-market experience as the analysis of
determinants of digital job search in Peru show that this particular group of jobseekers
disproportionally uses the Internet or cell phones less for job search purposes. Now, more than ever,
mobile phone technologies might constitute an effective tool to lessen the global digital divide and
empower vulnerable groups with access to tailored content-value information services.
- 31 -
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- 35 -
Non-digital T1 Digital T2 Digital T3 Control p-value of t-test p-value of F test
Baseline Variables DT1
DT2
DT3
DC
Treatment=Control [DT1
=DT2
=DT3
=DC
]
A. Socio-Demographic
sex (1=male) 0.57 0.52 0.55 0.54 0.62 0.65
age 27.04 25.33 26.27 25.55 0.01 0.01
years of schooling 12.15 12.09 12.06 11.94 0.36 0.76
single 0.71 0.76 0.73 0.73 0.82 0.46
have children 0.32 0.26 0.32 0.26 0.14 0.22
number of children 0.55 0.39 0.55 0.45 0.24 0.12
migrant 0.27 0.29 0.26 0.25 0.48 0.75
access to flush toilet 0.93 0.91 0.93 0.95 0.11 0.29
access to safe water 0.94 0.90 0.93 0.94 0.40 0.39
poverty index 0.06 -0.01 -0.02 0.03 0.68 0.64
B. Last Labor-Market Experience
worked ever 0.81 0.80 0.82 0.82 0.91 0.97
discouraged worker 0.11 0.14 0.13 0.13 0.85 0.85
unemployment duration (days) 75 105 104 99 0.60 0.22
montly income (in soles) 526 485 491 556 0.29 0.66
hours work per week 34.15 34.97 34.36 37.28 0.18 0.39
had accident insurance 0.18 0.16 0.16 0.18 0.61 0.87
had pension plan 0.25 0.26 0.21 0.23 0.90 0.70
had health insurance 0.25 0.25 0.20 0.21 0.64 0.52
had formal contract 0.76 0.73 0.63 0.66 0.21 0.02
white-collar worker 0.48 0.52 0.46 0.56 0.19 0.45
blue-collar worker 0.17 0.12 0.19 0.14 0.35 0.27
C. Digital technologies and job search
number of search methods 1.69 1.56 1.68 1.67 0.90 0.72
number of hours-week job search 5.68 5.18 5.48 5.68 0.63 0.79
number of hours-week in public channels 0.77 1.01 0.80 0.82 0.90 0.65
number of hours-week in private channels 4.90 4.01 4.67 4.84 0.52 0.28
use cell phone 0.95 0.95 0.96 0.97 0.14 0.43
use cell phone for job search 0.51 0.49 0.49 0.46 0.37 0.79
cell phone to contact prospective employers 0.55 0.57 0.57 0.60 0.39 0.83
cell phone to contact public LMIs 0.09 0.11 0.13 0.12 0.74 0.65
cell phone to contact private LMIs 0.27 0.28 0.25 0.24 0.47 0.85
use internet 0.83 0.85 0.84 0.84 0.98 0.86
use internet for job search 0.60 0.62 0.61 0.62 0.63 0.91
acess internet from home 0.24 0.29 0.24 0.20 0.12 0.30
access internet from internet cafes 0.74 0.70 0.74 0.79 0.13 0.35
access internet from work 0.02 0.01 0.01 0.01 0.83 0.62
N 345 188 303 354
Notes: The test of equal means for the experimental sample is based on a regression with treatment indicators on the right-hand side
DT1
refers to the short-non-digital treatmeng group, DT2
to the short-digital treatment group, DT3
to the enhance-digital treatment
group, and DT4
to the control group.
Table 1: Summary Statistics by Treatment Status
Digital Labor-Market Intermediation Program, Lima 2009-2010
Treatment Groups
- 36 -
Digital Internet Cell phones
internet+cell phones
A. Socio-Demograhic Characteristics
gender (1=men, 0=women) 0.023 -0.026 0.097***
(0.028) (0.036) (0.032)
age -0.003 0.000 -0.004
(0.002) (0.003) (0.003)
single 0.032 0.064 -0.038
(0.047) (0.063) (0.053)
has children 0.105* 0.188** -0.006
(0.058) (0.081) (0.076)
number of children -0.028 -0.076* -0.000
(0.029) (0.041) (0.036)
migrant 0.065 0.062 0.087*
(0.043) (0.061) (0.053)
years from migration -0.000 -0.000 -0.000
(0.003) (0.004) (0.003)
years of schooling 0.007 0.004 0.016**
(0.048) (0.007) (0.006)
poor 0.007 -0.145** 0.141***
(0.048) (0.063) (0.054)
less poor -0.036 -0.095* 0.066
(0.041) (0.052) (0.047)
B. Labor-market Characteristics
worked under formal contract 0.021 0.038 -0.014
(0.038) (0.050) (0.043)
had pension plan 0.084 0.048 0.006
(0.071) (0.101) (0.089)
had health insurance 0.007 0.077 0.023
(0.078) (0.095) (0.088)
worked as blue-collar -0.009 0.000 -0.011
(0.035) (0.047) (0.039)
never work -0.283*** -0.134** -0.293***
(0.047) (0.054) (0.042)
job gain expectations (1=high, 0=otherwise) 0.162*** 0.159*** 0.075**
(0.031) (0.038) (0.034)
continue…..
Table 2: Determinants of Digital Job Search, Probit Marginal Estimates (Baseline Data)
Labor-Market Intermediation Program, Lima 2009-2010
Marginal Effects
- 37 -
C. Cell phones Experience
Up to one year 0.097** 0.105* 0.097*
(0.036) (0.054) (0.050)
Between one and three years 0.043 0.023 0.025
(0.029) (0.039) (0.036)
D. Internet Experience
Has computer at home 0.279*** 0.521*** -0.124
(0.032) (0.033) (0.084)
use computer less than three years 0.005 0.148** -0.092
(0.060) (0.067) (0.069)
use computer between three and five years -0.031 0.046 -0.053
(0.063) (0.072) (0.071)
use computer more than 5 years 0.117** 0.220*** -0.010
(0.052) (0.063) (0.070)
Internet café in the neighborhood 0.331*** 0.688*** -0.066
(0.073) (0.055) (0.081)
Internet café one block away from neighboohood 0.095** 0.062 0.067
(0.040) (0.052) (0.051)
Internet café one to three blocks away from neighborhood 0.016 0.005 0.021
(0.043) (0.053) (0.053)
Internet café is 12 o 24 months old 0.007 0.086 -0.083
(0.063) (0.070) (0.070)
Internet café is more than 24 months old -0.029 0.032 0.007
(0.047) (0.057) (0.053)
E. Complementary between digital search channels
cell phone job search ----- 0.349*** ------
----- (0.033) ------
internet job search ------ ------ 0.393***
------ ------ (0.035)
N 1188 1188 1188
Notes: Standard errors in parentheses. Marginal effects from probit estimates using baseline data. The 'digital' outcome is defined as 1
for those who use Internet or cell phones for job search activities, 0 otherwise.
*** statistically significant at 1%, ** statistically significant at 5%, *statistically significant at 10%.
Table 2: Determinants of Digital Job Search, Probit Marginal Estimates (Baseline Data)
(continued)
- 38 -
Dep variable: employment
Overall Treatment 0.066** 0.063** 0.062** 0.056* 0.054* 0.055* 0.003 0.001 -0.002
(0.032) (0.032) (0.027) (0.032) (0.032) (0.031) (0.031) (0.031) (0.033)
Type of Treatment
non-digital treatment (DT1
) 0.041 0.035 0.034 0.046 0.042 0.040 -0.018 -0.027 -0.034
(0.039) (0.038) (0.035) (0.038) (0.038) (0.036) (0.038) (0.038) (0.044)
short-digital treatment (DT2
) 0.081* 0.088* 0.083* 0.057 0.061 0.060 0.034 0.033 0.034
(0.046) (0.046) (0.042) (0.046) (0.046) (0.046) (0.045) (0.045) (0.045)
enhance-digital treatment (DT3
) 0.086** 0.079** 0.081** 0.067* 0.064* 0.069* 0.008 0.005 0.008
(0.040) (0.039) (0.032) (0.039) (0.039) (0.037) (0.039) (0.039) (0.035)
N 1118 1118 118 1118 1118 118 1118 1118 118
Covariates No Yes Yes No Yes Yes No Yes Yes
Experimental Groups FE No No Yes No No Yes No No Yes
Notes: Standard errrors in parenthesis. Estimates based on a parametric cross-sectional estimator. The treatment indicator takes the value 1 for those benefiting from labor-market
intermediation, 0 otherwise. Socio-demographic covariates include age, gender, marital status, poverty index, whether individual has children, number of children, whether
individual is migrant, number of years since migration, years of schooling. Labor-market covariates include whether individual worked ever, an indicator for dicouraged worker,
whether individual had access to pension plan in last job, health insurance in last job, accident insurance in last job, whether individual worked under formal contract in last job,
blue-collar worker indicator in last job, white-collar worker indicator in last job. Clustered standard errors by day are considered when including experimental groups fixed effects
in columns 3, 6 and 9. *** statistically significant at 1%, ** statistically significant at 5%, *statistically significant at 10%.
Table 3: Labor market intermediation on employment outcome
Labor-Market Intermediation Program, Lima 2009-2010
Month # 1 Month # 2 Month # 3
- 39 -
dep. var: employment
Panel A: Gender
Intermediation 0.052 0.058 0.025 0.043 -0.011 0.001
(0.047) (0.043) (0.046) (0.042) (0.046) (0.046)
Intermediation*Men 0.019 0.007 0.052 0.022 0.022 -0.007
(0.065) (0.066) (0.064) (0.068) (0.063) (0.070)
Panel B: Work Experience
Intermediation -0.066 -0.073 0.052 0.072 -0.023 -0.016
(0.076) (0.087) (0.075) (0.078) (0.075) (0.075)
Intermediation*Workever 0.165** 0.165* 0.008 -0.020 0.035 0.016
(0.084) (0.095) (0.083) (0.089) (0.082) (0.078)
Panel C: Poor
Middle -0.005 -0.020 0.014 0.013 -0.020 -0.028
(0.074) (0.091) (0.073) (0.088) (0.072) (0.077)
High 0.029 -0.028 -0.019 -0.013 -0.107 -0.089
(0.078) (0.087) (0.077) (0.078) (0.076) (0.074)
Intermediation*Poor 0.054 0.053 0.061 0.064 -0.012 -0.019
(0.040) (0.040) (0.039) (0.041) (0.039) (0.039)
Intermediation*Middle 0.067 0.074 0.011 0.023 -0.004 0.017
(0.077) (0.083) (0.076) (0.080) (0.076) (0.074)
Intermediation*High 0.113 0.091 0.093 0.059 0.086 0.052
(0.083) (0.077) (0.082) (0.082) (0.081) (0.085)
p-value of F-test for equality of coeff. 0.819 0.915 0.750 0.914 0.546 0.691
N 1118 1118 1118 1118 1118 1118
covariates no yes no yes no yes
experimental groups FE no yes no yes no yes
Notes: Standard errrors in parenthesis. Estimates based on a parametric cross-sectional estimator. The treatment indicator takes the value 1 for
those benefiting from labor-market intermediation, 0 otherwise. Socio-demographic covariates include age, gender, marital status, poverty index,
whether individual has children, number of children, whether individual is migrant, number of years since migration, years of schooling.
Labor-market covariates include whether individual worked ever, an indicator for dicouraged worker, whether individual had access to a pension
plan in last job, health insurance in last job, accident insurance in last job, whether individual worked under formal contract in last job, blue-collar
worker indicator in last job, white-collar worker indicator in last job. Clustered standard errors by day are considered when including experimental
groups fixed effects in columns 2,4 and 6.
*** statistically significant at 1%, ** statistically significant at 5%, *statistically significant at 10%.
Table 4: Heterogenous Impacts
Labor-Market Intermediation Program, Lima 2009-2010
Month # 1 Month # 2 Month # 3
- 40 -
dep. var: monthly earnings (soles)
Overall Treatment 4 -10 28 11 34 16
(28) (27) (26) (24) (26) (24)
Type of Treatment
non-digital treatemnt (DT1
) 31 -6 36 0 42 13
(34) (35) (31) (32) (32) (33)
short-digital treatment (DT2
) -45 -44 -12 -4 5 -1
(39) (41) (37) (37) (36) (41)
enhance-digital treatment (DT3
) 6 5 44 33 44 30
(34) (34) (32) (30) (32) (28)
dep. var: matching skills
Overall Treatment 0.008 0.043 -0.014 0.014 -0.016 0.014
(0.046) (0.041) (0.043) (0.039) (0.041) (0.039)
Type of Treatment
non-digital treatement (DT1
) 0.046 -0.071 0.051 0.076 0.089 0.102*
(0.055) (0.051) (0.058) (0.054) (0.062) (0.056)
short-digital treatment (DT2
) 0.003 0.068 0.022 0.098 -0.014 0.060
(0.064) (0.079) (0.068) (0.077) (0.071) (0.083)
enhance-digital treatment (DT3
) -0.026 -0.001 -0.031 0.000 -0.012 0.031
(0.055) (0.049) (0.059) (0.053) (0.062) (0.062)
N 570 570 650 650 682 682
Covariates No Yes No Yes No Yes
Experimental Groups FE No Yes No Yes No Yes
Notes: Standard errrors in parenthesis. Estimates based on a parametric cross-sectional estimator conditional on working status.
The treatment indicator takes the value 1 for those benefiting from labor-market intermediation, 0 otherwise. Socio-demographic
covariates include age, gender, marital status, poverty index, whether individual has children, number of children, whether
individual is migrant, number of years since migration, years of schooling. Labor-market covariates include whether individual
worked ever, an indicator for dicouraged worker, whether individual had access to pension plan in last job, health insurance in
last job, accident insurance in last job, whether individual worked under formal contract in last job, blue-collar worker indicator
in last job, white-collar worker indicator in last job. Clustered standard errors by day are considered when including experimental
group fixed effects in columns 2,4 and 6.
*** statistically significant at 1%, ** statistically significant at 5%, *statistically significant at 10%.
Table 5: Labor-market intermediation on job matching efficiency
Labor-Market Intermediation Program, Lima 2009-2010
Month # 2 Month # 3Month # 1
- 41 -
Overall Treatment 0.053* 0.062* -0.012 0.013
(0.031) (0.032) (0.056) (0.060)
Type of Treatment
non-digital treatment (DT1
) 0.036 0.049 -0.008 0.028
(0.038) (0.038) (0.066) (0.065)
short-digital treatment (DT2
) 0.056 0.056 -0.022 0.051
(0.045) (0.046) (0.084) (0.094)
enhance-digital treatment (DT3
) 0.069* 0.079* -0.011 -0.029
(0.039) (0.042) (0.070) (0.082)
N 1140 1140 372 372
Covariates No Yes No Yes
Experimental Groups FE No Yes No Yes
Notes: Standard errrors in parenthesis. Estimates based on a parametric cross-sectional estimator. Internet-based search is
estimated over the full sample, while traditional search methods is estimated over the sample of unemployed three months
after the start of treatment. The treatment indicator takes the value 1 for those for those benefiting from labor-market
intermediation, 0 otherwise. Socio-demographic covariates include age, gender, marital status, poverty index, whether
individual has children, number of children, whether individual is migrant, number of years since migration, years of
schooling. Labor-market covariates include whether individual worked ever, an indicator for dicouraged worker, whether
individual had access to pension plan in last job, health insurance in last job, accident insurance in last job, whether
individual worked under formal contract in last job, blue-collar worker indicator in last job, white-collar worker indicator
in last job. Clustered standard errors by day are considered in columns 2 and 4.
*** statistically significant at 1%, ** statistically significant at 5%, *statistically significant at 10%.
Traditional search channelsInternet-based job search
Table 6: Impact of Labor-market intermediation on other intermediate outcomes
Labor-Market Intermediation Program, Lima 2009-2010
- 42 -
Appendix A: Some Theoretical Considerations
The model presented here is a (marginal) extension of a standard job-search model with endogenous
search intensity (Mortensen, 1986) to illustrate the theoretical effects of policies that aim at changing the rate
at which jobseekers are informed about job openings on search intensity and exit rate from unemployment.
Previous related literature has studied the effect of job offer arrival rates on the exit rate from unemployment
in the context of constant individual’s search efforts and a single job search channel. For this particular case,
Van den Berg (1994), shows minimal conditions on the wage offer distribution that ensure a positive effect on
the exit rate associated with an increase in the job offer arrival rate. Fougère et. al. (2009) extended the model
by considering two channels of job-search, public and private, with endogenous search effort only along the
private channel. Results from this model suggest that an increase in the public job offer arrival rate has only a
positive effect on the exit rate as long as the wage offer distributions in both channels are assumed to be
identical; otherwise, the direction of the effect is ambiguous.
We extended the model by considering two job search channels, public and private, with endogenous
search efforts in both search channels. In contrast to Fougère et. al. (2009), we consider that the job offer
arrival rate via the public channel is endogenous since the features of the public labor intermediation services
we are studying suggest that individuals need to allocate a complementary search effort to make effective a
job offer. In this study, the job offer arrival rate depends on the individual’s search effort, the type of
technology employed by the public labor mediation services, and the set of information used to match
individuals to jobs. We consider an unemployed individual who does not receive any UI benefits and is
actively searching for a job using both private and public labor intermediation services. Unemployed
individuals using public intermediation services are contacted and informed about job openings according to a
Poisson process with endogenous rate 1 1( , )s that depends on the search intensity effort 1s and the overall
channel-specific search efficiency 1 , which in turn depends on the technology used to convey the information
- 43 -
and the set of information employed to match job openings . We assume that the overall channel-
specific search efficiency level can be improved with information communication technologies 1, 0 , and
by employing a broader set of information on job openings 1, 0 to match individuals to jobs. The arrival
rate of effective job offers is defined as 1 1 1( , )q s where 1q is the probability that a job contact via public
mediation service leads to a job offer.
Likewise, unemployed individuals using private job-search methods are informed about job openings
at rate 2 2s , which depends on the search intensity effort 2s and an overall constant channel-specific search
efficiency level 2 . The arrival rate of effective job offers is defined as 2 2 2q s where 2q is the probability that
a job contact via private job-search leads to an effective job offer. Job offers along this two channels of search
are characterized as random draws from wage offer distributions 1( )F w and 2 ( )F w , respectively.14
Search
intensity effort is costly and can be summarized by the cost function 1 2( , )c s s with increasing and convex
arguments such that20, 0, 0, 0
i i i i j i i i j i js s s s s s s s s s sc c c c c c . In the absence of UI benefits as it is the case
here, let z be the value of leisure and r the discount rate. An optimal search strategy is a choice of an
intensity of search effort and reservation wage*w . Let U and ( )W w denote the present discounted value of
income when unemployed and employed, respectively. The present-discounted value from unemployment U
satisfies the bellman equation
1, 2
1 2 1 1 1 1 1 2 2 2 2 20
0 0
( , ) ( , ) max[0, ( ) ] ( ) max[0, ( ) ] ( )s s
rU Max z c s s q s W x U dF x q s W x U dF x
(1)
14
We do not make any assumption regarding the distributions functions F1 and F2 .
- 44 -
By applying the reservation wage property * *( ) /U W w w r to equation (1), we can state that the
reservation wage, *w , is solution to
1, 2 * *
* * *1 1 1 2 2 21 2 1 2
0
( , )( , ) ( ) ( ) ( ) ( )
s sw w
q s q sw Max z c s s x w dF x x w dF x
r r
(2)
The first order conditions for optimal search intensities efforts *
1s and *
2s equates its marginal return and
cost, and can be written as
1*
2*
*1 11 1
*2 22 2
( , ): ( ) ( ) 0
: ( ) ( ) 0
s
w
s
w
qs c x w dF x
r
qs c x w dF x
r
(3)
The exit rate from unemployment for an individual that use public labor intermediation services as well as
alternative private job-search methods is defined as
* *
1 1 1 1 2 2 2 2( , )[1 ( )] [1 ( )]q s F w q s F w (4)
The theoretical effect of policies that may affect the rate at which unemployed individuals are informed about
job openings (e.g., SMS technology) on the exit rate from unemployment is defined as
*
1 1 2 1 21 1 1 1 2 2 1 1 2 2* *
( , )( , ) [ ( , ) ]
s s a ad wa s a a s s
d w w
(5)
where *[1 ( )]i i ia q F w . By construction we know that the overall search efficiency level 1 can be
improved by information technology (e.g., SMS) used to convey information on job opportunities to
unemployed individuals 1( , ) / 0 . Then, a comparative static exercise (available under request),
suggest that the search effort allocated to the public job-search channel increases with the technology that
improves the rate at which unemployed individuals are contacted and informed about job openings,
1 / 0s .
- 45 -
The intuition for this result suggest that as long as the SMS technology improves the overall search efficiency
in the public search channel, those using this channel will tend to allocate higher search efforts to it.
Conversely, it is expected that individuals’ search efforts allocated to alternative private search channels will
be crowed-out since 2 / 0s . Further, as the technology improves the rate at which individuals are
informed about job openings, we expect a more patient unemployed individual waiting to consider a better
wage offer, as the increase in the reservation wage suggest, * / 0w . Hence, the sign of expression (5) is
uncertain, and clearly hinges on the magnitudes of the reservation wage response to policy and the crowding-
out effect on private job-search effort. This uncertainty only has been resolved in models with a single job-
search channels and constant job offer arrival rate (Van den Berg, 1994) or models with two job-search
channels and an endogenous job offer arrival rate in only one channel (Fougère et. al. , 2009). Yet here with
endogenous job offers arrival rates in both search channels, finding conditions to tell the direction of
expression (5) is not clear. Therefore, we have to turn to an empirical approach to gain additional insights.
- 46 -
Month # 1 Month # 2 Month # 3
Dep variable: employment
Overall Treatment 0.055** 0.047* -0.010
(0.027) (0.028) (0.033)
Type of Treatment
non-digital treatment (DT1
) 0.024 0.03 -0.044
(0.035) (0.037) (0.045)
short-digital treatment (DT2
) 0.073* 0.049 0.022
(0.042) (0.043) (0.044)
enhance-digital treatment (DT3
) 0.078** 0.066* 0.005
(0.034) (0.038) (0.036)
N 1118 1118 118
Covariates Yes Yes Yes
Experimental Groups FE Yes Yes Yes
Notes: Clustered standard errrors in parenthesis. The unrestricted difference-in-differences model follows from Lalonde (1986). The right-hand
side variables include the same set as reported in table 3 plus the baseline work status.
*** statistically significant at 1%, ** statistically significant at 5%, *statistically significant at 10%.
Table Appendix A1: Labor market intermediation on employment outcome, unrestricted difference-in-differences
Labor-Market Intermediation Program, Lima 2009-2010